LogitBoost

Early stopping in L2 Boosting. It is well known that the boosting-like algorithms, such as AdaBoost and many of its modifications, may over-fit the training data when the number of boosting iterations becomes large. Therefore, how to stop a boosting algorithm at an appropriate iteration time is a longstanding problem for the past decade applied model selection criteria to estimate the stopping iteration for L 2 Boosting, but it is still necessary to compute all boosting iterations under consideration for the training data. Thus, the main purpose of this paper is focused on studying the early stopping rule for L 2 Boosting during the training stage to seek a very substantial computational saving. The proposed method is based on a change point detection method on the values of model selection criteria during the training stage. This method is also extended to two-class classification problems which are very common in medical and bioinformatics applications. A simulation study and a real data example to these approaches are provided for illustrations, and comparisons are made with LogitBoost.


References in zbMATH (referenced in 18 articles )

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  1. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  2. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  3. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  4. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  5. Contreras-Torres, Ernesto: Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC (2018)
  6. Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure; De Bin, Riccardo: On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models (2018)
  7. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  8. Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R.; Barigye, Stephen J.; Cubillán, Néstor; Alvarado, Ysaías J.: Novel 3D bio-macromolecular bilinear descriptors for protein science: predicting protein structural classes (2015)
  9. Hayat, Maqsood; Tahir, Muhammad; Khan, Sher Afzal: Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces (2014)
  10. Zhang, Shengli; Liang, Yunyun; Yuan, Xiguo: Improving the prediction accuracy of protein structural class: approached with alternating word frequency and normalized Lempel-Ziv complexity (2014)
  11. Liu, Zhi-Xin; Liu, Song-lei; Yang, Hong-Qiang; Bao, Li-Hua: Using protein granularity to extract the protein sequence features (2013)
  12. Chang, Yuan-Chin Ivan; Huang, Yufen; Huang, Yu-Pai: Early stopping in (L_2)Boosting (2010)
  13. Sahu, Sitanshu Sekhar; Panda, Ganapati: A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction (2010)
  14. Mizianty, Marcin J.; Kurgan, Lukasz A.: Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences (2009) ioport
  15. Chen, Chao; Chen, Li-Xuan; Zou, Xiao-Yong; Cai, Pei-Xiang: Predicting protein structural class based on multi-features fusion (2008)
  16. Cai, Yu-Dong; Feng, Kai-Yan; Lu, Wen-Cong; Chou, Kuo-Chen: Using LogitBoost classifier to predict protein structural classes (2006)
  17. Chen, Chao; Tian, Yuan-Xin; Zou, Xiao-Yong; Cai, Pei-Xiang; Mo, Jin-Yuan: Using pseudo-amino acid composition and support vector machine to predict protein structural class (2006)
  18. Wang, Shuang-Quan; Yang, Jie; Chou, Kuo-Chen: Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition (2006)